1. Field of the Invention
The present invention relates to a system and method for improving the signal-to-clutter ratio of electromagnetic signals received by an antenna array, and more particularly, to a system and method for improving the signal-to-clutter ratio of electromagnetic signals degraded by array calibration errors and multi-path interference.
2. Discussion of the Related Art
Various signal-processing techniques have been developed to process signal returns using antenna arrays. Efforts are generally made to reduce the interference received with the signal. Interference can include any electromagnetic energy that interferes with the desired signal, such as noise, clutter, and jamming. When a receiver is used in a moving environment, such as in an aircraft, environmental noise tends to be enhanced. Suppressing these undesirable signals has proven to be particularly challenging.
Space-time adaptive processing (STAP) is a particular signal processing technique designed to extract return signals from a target object by weighting a set of return signals from an antenna array to enhance the peaks in the direction of expected targets and generate nulls in the radiation pattern in the direction of noise or interference sources. Non-adaptive techniques use fixed weights, whereas adaptive techniques attempt to calculate weights based on the return signal set.
To calculate the adaptive weights, STAP techniques typically combine several pulse samples received by the array elements over a designated period of time. Generally, adaptive weights are calculated through the relationship R{right arrow over (w)}={right arrow over (s)}, where {right arrow over (s)} is the beam steering vector, R is the covariance matrix, and {right arrow over (w)} is the weight vector. In order to identify the adaptive weights this relationship is simply manipulated to the following: {right arrow over (w)}=Rxe2x88x921{right arrow over (s)}.
In a radar environment used to detect and track moving objects, the process of calculating and altering the weights must be done in real-time. STAP typically includes more adaptive parameters than can be estimated with a given amount of data in a non-stationary environment. There is simply insufficient data to accurately estimate these parameters to the accuracy necessary to provide a performance improvement over that obtainable with reduced degree of freedom processing. In addition, the processing required for full-degrees-of-freedom real-time processing is extremely difficult.
White noise gain constraints are popular methods for calculating adaptive weights when processing reduced rank calculations. Diagonal loading is the simplest form of a white noise gain constraint and is commonly used. Diagonal loading applies a quadratic constraint to restrain the effective increase in the background noise to allow suppression via deep nulls of highly anisotropic interference.
The basic optimization parameters for the adaptive weights are as follows:             min      w        ⁢                  w        H            ⁢      R      ⁢              xe2x80x83            ⁢      w                          s        .        t        .                  xe2x80x83                ⁢                  w          H                    ⁢      d        =    1                      w        H            ⁢      w        ≤    c  
where
w=the adaptive weight vector;
d=the target object steering vector; and
H=the Hermitian transpose.
Through a standard optimization the resulting adaptive weights can be calculated via diagonal loading of the covariance R. The weights are given by:   w  =                              (                      R            +                          λ              ⁢                              xe2x80x83                            ⁢              I                                )                          -          1                    ⁢      d                                (                                    d              H                        ⁢                          (                              R                +                                  λ                  ⁢                                      xe2x80x83                                    ⁢                  I                                            )                                )                          -          1                    ⁢      d      
where xcex is the smallest nonnegative number chosen to satisfy the quadratic white noise gain constraint wHwxe2x89xa6c. The value for c is adjusted to give the desired beamformer performance in terms of trading off the noise response versus the reduction of interference power. Often, instead of directly establishing a value for c, the resulting optimization works with an implied he value of xcex that is typically chosen by estimating the noise floor and selecting xcex to be in the range of xe2x88x9210 to +10 dB below or above the noise floor. When a value for c is explicitly given the solution for the Lagrange multiplier xcex depends on the direction implicit in the steering vector d.
The use of a single value xcex to calculate the quadratic white noise gain constraint is common, but not optimal. Additionally, estimation of the noise floor must be performed in order to locate the best white noise gain value by either choosing a value for c and solving explicitly for xcex or by selecting xcex explicitly.
Further complicating the signal processing, input signals received by an antenna array tend to show highly variable power histories indicative of constructive and destructive multipath interference. Signal cancellation occurs due to multipath, which is coherent with a main beam signal. This causes the weights to xe2x80x9chuntxe2x80x9d excessively for short periods of integration. Longer integration periods reduce hunting, but slow reaction to changing interference environments. Furthermore, clutter suppression is degraded because array calibration errors raise sidelobe levels thereby mismatching the array steering vectors to the environment.
Additionally, these difficulties are present whether using a monostatic or bistatic system; however, a solution in the bistatic system is significantly more difficult to accomplish. For these and other reasons, obtaining improved clutter suppression from adaptive processing in a multipath environment is difficult.
These and other deficiencies exist in current adaptive processing systems. Therefore, a solution to these problems is needed providing a reduced rank adaptive processing system and method specifically designed to more accurately calculate the signals received by an antenna.
Accordingly, the present invention is directed to a system and method for providing calibrated, reduced rank adaptive processing. In particular, in the bistatic case, the present invention calibrates the signals received by the antenna array using the signals themselves as the calibration source, and adaptively processes the signals by calculating adaptive weights from a reduced rank approximation of the covariance matrix through a partial singular value decomposition.
In one embodiment, the invention comprises a signal processing system for adaptively processing signals received by an antenna array and organized into a data matrix that enhances the signal-to-noise ratio of the signals, comprising an antenna array, and a signal processor connected to the antenna array that includes a phase calibration element for correcting phase errors in the received signals using the received signals as the calibration source and an adaptive processing element for calculating adaptive weights from a reduced rank approximation of a factorization of a covariance matrix calculated from a partial singular value decomposition of the data matrix.
A further embodiment of the present invention comprises a method for processing received signals received by an antenna array for improving the signal-to-noise ratio of the received signals, comprising the steps of automatically calibrating the phase of the received signals to correct for phase errors, and adaptively processing the signals with reduced degrees of freedom.
Additional features and advantages of the invention will be set forth in the description that follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof, as well as the appended drawings.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.